Using assumptions underlying hazard analysis

Analyses are types of calculations but may be comparative studies, predictions, and estimations. Examples are stress analysis, reliability analysis, hazard analysis. Analyses are often performed to detect whether the design has any inherent modes of failure and to predict the probability of occurrence. The analyses assist in design improvement and the prevention of failure, hazard, deterioration, and other adverse conditions. Analyses may need to be conducted as the end-use conditions may not be reproducible in the factory. Assumptions may need to be made about the interfaces, the environment, the actions of users, etc. and analysis of such conditions assists in determining characteristics as well as verifying the inherent characteristics. (See also in Part 2 Chapter 14 under Detecting design weaknesses.)... 

Setting up a safety information system for a single project or product may be easier. The effort starts in the development process and then is passed on for use in operations. The information accumulated during the safety-driven design process provides the baseline for operations, as described in chapter 12. For example, the identification of critical items in the hazard analysis can be used as input to the maintenance process for prioritization. Another example is the use of the assumptions underlying the hazard analysis to guide the audit and performance assessment process. But first the information needs to be recorded and easily located and used by operations personnel. 

Proportional hazards model. Generally, a regression model used in survival analysis whereby it is assumed that the hazard functions of individuals under study are proportional to each other over time. (This is equivalent to assuming that the log-hazard functions differ by a constant.) More specifically, and in the form originally proposed by Cox (1972), no further definite assumption is made about the hazard functions themselves. One of the most commonly used statistical techniques in survival analysis. 

An underlying assumption of all accident models is that there are common patterns in accidents and that they are not simply random events. Accident models impose patterns on accidents and influence the factors considered in any safety analysis. Because the accident model influences what cause(s) is ascribed to an accident, the countermeasures taken to prevent future accidents, and the evaluation of the risk in operating a system, the power and features of the accident model used will greatly affect our ability to identify and control hazards and thus prevent accidents. 

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